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Variance reduction in MCMC

Author

Listed:
  • Mira Antonietta

    (Department of Economics, University of Insubria, Italy)

  • Tenconi Paolo

    (University of Switzerland)

  • Bressanini Dario

    (University of Insubria, Italy)

Abstract

We propose a general purpose variance reduction technique for MCMC estimators. The idea is obtained by combining standard variance reduction principles known for regular Monte Carlo simulations (Ripley, 1987) and the Zero-Variance principle introduced in the physics literature (Assaraf and Caffarel, 1999). The potential of the new idea is illustrated with some toy examples and an application to Bayesian estimation

Suggested Citation

  • Mira Antonietta & Tenconi Paolo & Bressanini Dario, 2003. "Variance reduction in MCMC," Economics and Quantitative Methods qf0310, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0310
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    File URL: https://www.eco.uninsubria.it/RePEc/pdf/QF2003_29.pdf
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    Keywords

    Markov chain Monte carlo; Metropolis-Hastings algorithm; Variance reduction; Zero-Variance principle;
    All these keywords.

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